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Wang S, Yan X, Jiao X, Yang H. Experimental Study of the Implantation Process for Array Electrodes into Highly Transparent Agarose Gel. MATERIALS (BASEL, SWITZERLAND) 2024; 17:2334. [PMID: 38793401 PMCID: PMC11123045 DOI: 10.3390/ma17102334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/09/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024]
Abstract
Brain-computer interface (BCI) technology is currently a cutting-edge exploratory problem in the field of human-computer interaction. However, in experiments involving the implantation of electrodes into brain tissue, particularly high-speed or array implants, existing technologies find it challenging to observe the damage in real time. Considering the difficulties in obtaining biological brain tissue and the challenges associated with real-time observation of damage during the implantation process, we have prepared a transparent agarose gel that closely mimics the mechanical properties of biological brain tissue for use in electrode implantation experiments. Subsequently, we developed an experimental setup for synchronized observation of the electrode implantation process, utilizing the Digital Gradient Sensing (DGS) method. In the single electrode implantation experiments, with the increase in implantation speed, the implantation load increases progressively, and the tissue damage region around the electrode tip gradually diminishes. In the array electrode implantation experiments, compared to a single electrode, the degree of tissue indentation is more severe due to the coupling effect between adjacent electrodes. As the array spacing increases, the coupling effect gradually diminishes. The experimental results indicate that appropriately increasing the velocity and array spacing of the electrodes can enhance the likelihood of successful implantation. The research findings of this article provide valuable guidance for the damage assessment and selection of implantation parameters during the process of electrode implantation into real brain tissue.
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Affiliation(s)
| | - Xuan Yan
- Beijing Key Laboratory of Lightweight Multi-Functional Composite Materials and Structures, Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China; (S.W.); (X.J.)
| | | | - Heng Yang
- Beijing Key Laboratory of Lightweight Multi-Functional Composite Materials and Structures, Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China; (S.W.); (X.J.)
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Liang J, Bian M, Chen H, Yan K, Li Z, Qin Y, Wang D, Zhu C, Huang W, Yi L, Sun J, Mao Y, Hao Z. Gradient boosting DD-MLP Net: An ensemble learning model using near-infrared spectroscopy to classify after-stroke dyskinesia degree during exercise. JOURNAL OF BIOPHOTONICS 2023; 16:e202300029. [PMID: 37280169 DOI: 10.1002/jbio.202300029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/25/2023] [Accepted: 06/02/2023] [Indexed: 06/08/2023]
Abstract
This study aims to develop an automatic assessment of after-stroke dyskinesias degree by combining machine learning and near-infrared spectroscopy (NIRS). Thirty-five subjects were divided into five stages (healthy, patient: Brunnstrom stages 3, 4, 5, 6). NIRS was used to record the muscular hemodynamic responses from bilateral femoris (biceps brachii) muscles during passive and active upper (lower) limbs circular exercise. We used the D-S evidence theory to conduct feature information fusion and established a Gradient Boosting DD-MLP Net model, combining the dendrite network and multilayer perceptron, to realize automatic dyskinesias degree evaluation. Our model classified the upper limb dyskinesias with high accuracy: 98.91% under the passive mode and 98.69% under the active mode, and classified the lower limb dyskinesias with high accuracy: 99.45% and 99.63% under the passive and active modes, respectively. Our model combined with NIRS has great potential in monitoring the after-stroke dyskinesias degree and guiding rehabilitation training.
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Affiliation(s)
- Jianbin Liang
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Minjie Bian
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Hucheng Chen
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Kecheng Yan
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Zhihao Li
- School of Medicine, Foshan University, Foshan, China
| | - Yanmei Qin
- School of Medicine, Foshan University, Foshan, China
| | - Dongyang Wang
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Chunjie Zhu
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Wenzhu Huang
- The Fifth Affiliated Hospital of Foshan, Foshan University, Foshan, China
| | - Li Yi
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, China
| | - Jinyan Sun
- School of Medicine, Foshan University, Foshan, China
| | - Yurong Mao
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Zhifeng Hao
- College of Science, Shantou University, Shantou, China
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Bates M, Sunderam S. Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review. Front Hum Neurosci 2023; 17:1121481. [PMID: 37484920 PMCID: PMC10357516 DOI: 10.3389/fnhum.2023.1121481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 06/01/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction Various neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer. Methods Here, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement. Results A majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon. Discussion We conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.
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Guo L, Zhang B, Wang J, Wu Q, Li X, Zhou L, Xiong D. Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment. J Clin Med 2022; 11:jcm11247467. [PMID: 36556083 PMCID: PMC9783419 DOI: 10.3390/jcm11247467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/10/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
In order to solve the shortcomings of the current clinical scale assessment for stroke patients, such as excessive time consumption, strong subjectivity, and coarse grading, this study designed an intelligent rehabilitation assessment system based on wearable devices and a machine learning algorithm and explored the effectiveness of the system in assessing patients’ rehabilitation outcomes. The accuracy and effectiveness of the intelligent rehabilitation assessment system were verified by comparing the consistency and time between the designed intelligent rehabilitation assessment system scores and the clinical Fugl−Meyer assessment (FMA) scores. A total of 120 stroke patients from two hospitals participated as volunteers in the trial study, and statistical analyses of the two assessment methods were performed. The results showed that the R2 of the total score regression analysis for both methods was 0.9667, 95% CI 0.92−0.98, p < 0.001, and the mean of the deviation was 0.30, 95% CI 0.57−1.17. The percentages of deviations/relative deviations falling within the mean ± 1.96 SD of deviations/relative deviations were 92.50% and 95.83%, respectively. The mean time for system assessment was 35.00% less than that for clinician assessment, p < 0.05. Therefore, wearable intelligent machine learning rehabilitation assessment has a strong and significant correlation with clinician assessment, and the time spent is significantly reduced, which provides an accurate, objective, and effective solution for clinical rehabilitation assessment and remote rehabilitation without the presence of physicians.
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Affiliation(s)
- Liquan Guo
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230052, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Bochao Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230052, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jiping Wang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230052, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Qunqiang Wu
- Department of Rehabilitation Medicine, Tangdu Hospital Airforce Medicine University, Xi’an 710032, China
| | - Xinming Li
- Department of Rehabilitation Medicine, Xi’an Gaoxin Hospital, Xi’an 710065, China
| | - Linfu Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China
- Correspondence: (L.Z.); (D.X.); Tel.: +86-18662576055 (D.X.)
| | - Daxi Xiong
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230052, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Correspondence: (L.Z.); (D.X.); Tel.: +86-18662576055 (D.X.)
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